The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction. Such cues are of utmost importance to decision-making since they provide insights on the features that influenced most certain forecasts and let the user decide if the forecast can be trusted. Though many techniques were developed to explain black-box models, little research was done on assessing the quality of those explanations and their influence on decision-making. We propose an ontology and knowledge graph to support collecting feedback regarding forecasts, forecast explanations, recommended decision-making options, and user actions. This way, we provide means to improve forecasting models, explanations, and recommendations of decision-making options. We tailor the knowledge graph for the domain of demand forecasting and validate it on real-world data.

中文翻译：

---

XAI-KG：支持XAI和制造业决策的知识图

越来越多地采用人工智能需要准确的预测，并且需要了解这种预测背后的人工智能模型的原因。可解释人工智能（XAI）旨在为模型发布特定预测的原因提供提示。这些提示对于决策至关重要，因为它们提供了对影响大多数特定预测的功能的见解，并让用户决定是否可以信任该预测。尽管开发了许多技术来解释黑匣子模型，但很少进行评估这些解释的质量及其对决策影响的研究。我们提出了一个本体和知识图，以支持收集有关预测，预测解释，建议的决策选项和用户操作的反馈。这条路，我们提供了改进预测模型，解释和决策选择建议的方法。我们针对需求预测领域量身定制知识图，并根据实际数据对其进行验证。